University of Groningen
Trends in Frailty and its Association with Mortality
Hoogendijk, Emiel O; Stolz, Erwin; Oude Voshaar, Richard C; Deeg, Dorly J H; Huisman, Martijn; Jeuring, Hans W
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10.1093/aje/kwab018
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Hoogendijk, E. O., Stolz, E., Oude Voshaar, R. C., Deeg, D. J. H., Huisman, M., & Jeuring, H. W. (2021). Trends in Frailty and its Association with Mortality: Results From the Longitudinal Aging Study Amsterdam (1995-2016). American Journal of Epidemiology. https://doi.org/10.1093/aje/kwab018
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Trends in Frailty and its Association with Mortality: Results From the Longitudinal Aging Study Amsterdam (1995-2016)
Emiel O. Hoogendijk, Erwin Stolz, Richard C. Oude Voshaar, Dorly J.H. Deeg, Martijn Huisman, and Hans W. Jeuring
Correspondence to: Dr. Emiel O. Hoogendijk, Department of Epidemiology & Data Science, Amsterdam UMC – location VU University Medical Center, P.O. Box 7057, 1007MB
Amsterdam, the Netherlands (email: e.hoogendijk@amsterdamumc.nl)
Author affiliations: Department of Epidemiology and Data Science, Amsterdam Public Health research institute, Amsterdam UMC - location VU University Medical Center, Amsterdam, the Netherlands (Emiel O. Hoogendijk, Dorly D.J.H. Deeg, Martijn Huisman); Institute of Social Medicine and Epidemiology, Medical University of Graz, Graz, Austria (Erwin Stolz); Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (Richard C. Oude Voshaar, Hans W. Jeuring); Department of Sociology, Faculty of Social Sciences, Vrije Universiteit Amsterdam, the Netherlands (Martijn Huisman).
Funding: Emiel O. Hoogendijk was supported by an NWO/ZonMw Veni fellowship, grant number 91618067. The Longitudinal Aging Study Amsterdam (LASA) is largely supported by a grant from the Netherlands Ministry of Health, Welfare and Sports, Directorate of Long-Term Care.
Conflicts of interest: None declared.
Running head: Trends in Frailty and Mortality
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© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License
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ABSTRACT
The aim of the current study was to investigate trends in frailty and its relationship with mortality among older adults aged 64-84 years across a period of 21 years. Data from 1995 to 2016 were used from the Longitudinal Aging Study Amsterdam. A total of 7,742 observations of 2,874 respondents in the same age range (64-84 years) across six measurement waves were included. Frailty was measured with a 32-item frailty index, with a cut-point of ≥0.25 to indicate frailty. The outcome measure was 4-year mortality. Generalized Estimating Equation analyses showed that among older adults aged 64-84 years the 4-year mortality rate declined between 1995 and 2016, while the prevalence of frailty increased. Across all measurement waves, frailty was associated with 4-year mortality (Odds Ratio: 2.79, 95% Confidence Interval: 2.39, 3.26). There was no statistically significant interaction effect between frailty and time on 4-year mortality, indicating a stable association between frailty and mortality. In more recent generations of older adults, frailty prevalence rates were higher, while excess mortality rates of frailty remained the same. This is important information for health policy makers and clinical practice, as it shows that continued efforts are needed to reduce frailty and its negative health consequences.
Key words: Cohort study; Frailty index; Frail older adults; Mortality; Trend
Abbreviations: CI: confidence interval, GEE: generalized estimating equation, LASA: Longitudinal Aging Study Amsterdam, OR: odds ratio, SD: standard deviation
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In aging societies, the concept of frailty has gained increased attention (1). Frailty in older adults is defined as a decrease in reserve capacity in multiple physiological systems and elevated vulnerability to stressors (2). It is associated with greater healthcare use and various adverse outcomes (1). For instance, frailty is one of the most important contributors to mortality in later life (3, 4).
In recent decades, life-expectancy has increased in most developed countries, and this also applies to the life-expectancy at age 65 (5). It has been suggested that this positive trend in life-expectancy is the result of better living circumstances and improved medical care (5). For example, premature mortality from chronic diseases such as diabetes and cardiovascular disease has declined due to better medical treatment (6, 7). However, at the same time, some studies have indicated that the increase in life-expectancy in older adults is accompanied by more years spent in poor physical health and higher rates of multimorbidity (5, 8, 9). Not much is known about the impact of these developments on frailty prevalence and frailty-related mortality.
Monitoring trends in frailty and its association with mortality is important for health policy makers, to be able to make projections about future healthcare use. So far, this topic received little attention in research. The few studies that have investigated birth cohort differences in frailty have found mixed results. One study that compared two cohorts of 70-year-olds in Sweden observed stable frailty levels (10), while studies in the UK and Hong Kong indicated that frailty is increasing in more recent cohorts of older adults (11-13). All these studies measured frailty with the frailty index, a commonly used frailty measure based on the deficit accumulation approach (14). It counts age-related signs, symptoms, diseases and disabilities, and is regarded a sensitive frailty instrument (15, 16).
Two of the above-cited studies have also investigated cohort differences in the frailty-mortality relationship, and found that the association between frailty and frailty-mortality remained
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the same or became slightly weaker (10, 11). However, both studies were only based on two time-points, 20 to 30 years apart, which makes it difficult to identify trends. For that purpose, a minimum of three observations is needed.
The Longitudinal Aging Study Amsterdam (LASA) is one of the few cohort studies in older populations that has data available on frailty - measured by the frailty index - and mortality in the same age group at multiple time points, because of its cohort-sequential design (17, 18). Therefore, using data from six time-points in LASA, the aim of the current study was to investigate trends in frailty and its relationship with mortality among older adults aged 64-84 years across a period of 21 years.
METHODS Study population
We used data from LASA, which is a nationally representative study on physical, emotional, cognitive and social functioning of older adults in the Netherlands. The sampling and
measurements of LASA have been described elsewhere in greater detail (17, 19). In short, the study started in 1992 with a survey among older adults aged 55-84 years, based on a
representative sample of the Dutch older population (n= 3,107). The data collection consists, amongst others, of face-to-face interviews and clinical tests in the home of the participants. Follow-up measurements are collected approximately every 3 years. A refresher cohort aged 55-64 years from the same sampling frame was added to the original sample in 2002-2003. This new cohort has the same follow-up schedule as the original cohort, with follow-up measurements every 3 years. As of the second LASA measurement wave (1995-1996) it is possible to measure frailty in LASA participants, due to some changes in measurement
instruments. LASA is conducted in line with the Declaration of Helsinki and was approved by
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the medical ethics committee of the VU University medical center. Informed consent was obtained from all study participants.
For the current study, we selected participants aged 64-84 years at each measurement wave between 1995 and 2012 (T1: 1995-1996, T2: 1998-1999, T3: 2001-2002, T4: 2005-2006, T5: 2008-2009 and T6: 2011-2012), to make the samples comparable in age over time. This resulted in partly independent samples, because at each measurement wave “new” participants (i.e., people who turned 64 years old) were included. Up to 2002, the newly included participants were from the original LASA cohort, and from 2005 they were from the LASA refresher cohort that was added in 2002-2003. On average, participants were included in 2.6 out of 6 measurement waves. The overlap between T1 and T6 was 14.1%. Participants were included in the analyses if they had valid data on mortality, frailty and demographic characteristics. The pooled dataset included 7,742 observations across six measurement waves from 2,874 participants. The number of participants at each wave varied from 1,141 to 1,549. Between measurement waves, 346 eligible participants (age between 64-84 years) dropped out because of other reasons than mortality (e.g., refusal, inability to contact). The proportion of non-mortality attrition was similar across measurement waves. Since the association between frailty and 4-year mortality was studied at each measurement wave, the current study has a 21-year time span (1995-2016). An overview of the cohort-sequential design and
mortality is provided in Figure 1.
Measures
Vital status and date of death were obtained from municipality registers up to January 2017. Its ascertainment was 99.4% complete. Mortality in LASA is very similar to that of the Dutch general older population (17). For all participants, we determined at each measurement wave 4-year mortality (yes/no) since the date of the interview. We chose 4-year mortality to make
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maximum use of the data, and to have a sufficient number of events to analyze. Moreover, from a clinical point of view, the short-medium follow-up time is the most relevant, as it still offers possibilities to intervene and mitigate risks.
Frailty was measured by the frailty index. This frailty measure is based on the deficit accumulation approach, and has been validated in LASA (20). A 32-item frailty index was constructed following a standard procedure (21). The idea of the frailty index is that its content is not fixed. As long as certain requirements are met, such as a minimum of 30 age-related health deficits covering multiple domains or organ systems, the exact combination of health deficits does not matter. Various studies have shown that key characteristics of the frailty index are consistent across datasets with different frailty index operationalization (22). The frailty index in LASA consists of 32 health deficits from the physical, mental and
cognitive domain: self-reported chronic conditions (11 items), functional limitations (6 items), self-rated health (2 items), mental health (6 items from the Center for Epidemiologic Studies Depression scale), physical activity (1 item), memory complaints (1 item), cognition
measured by sub-domains from the Mini-Mental State Examination (4 items), and physical performance measured by gait speed (1 item). All deficits were scored between 0 and 1, with 0 indicating the absence of the deficit, and 1 the presence of the deficit. Details on all items and cut-points of the LASA frailty index have been published previously (20). For the
calculation of the frailty index, we allowed a maximum of 20% missing variables (≤ 6 items). This is a commonly used criterion, allowing for maximum use of available data (23).
However, for most observations in the current study there were no missing variables (91.7% of the observations) or only 1-2 missing variables (7.7%) out of the total of 32 items of the frailty index. Frailty scores were calculated as follows: the sum of the health deficit scores were divided by the total number of items that were measured in a person (thereby taking into account the number of missing items, if any). For example, if a participant presents with 9
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health deficits out of 32 items, the frailty index score is 9/32= 0.28. We applied the commonly used ≥0.25 cut-off to indicate the presence of frailty (24), as well as cut-offs to distinguish prefrailty (0.08-0.24) (24).
Statistical analysis
Descriptive analyses were conducted to characterize the study sample at each measurement wave. Trends in frailty were investigated using Generalized Estimating Equation (GEE) analysis. All trend analyses were done for the total population and stratified by sex, because of the commonly observed sex differences in frailty (25). Logistic GEE analysis was done using a stationary M-dependent (Toeplitz) correlation structure, which accounts for within-subject correlations. Since we had six observations, this was a 5-dependent correlation structure. Although GEE and random effect models are both appropriate methods to study trends over time, it is more straightforward to estimate population average effects using GEE in case of binary outcomes (26). To show trends in frailty, we performed a model that
included time, age, and sex (where appropriate). The continuous time variable represents the increase in study years (0, 3, 6, 10, 13 and 16 years).
To investigate trends in the frailty-mortality relationship, again logistic GEE analyses with a 5-dependent correlation structure were performed, and three models were tested. In the first model, frailty was included, adjusted for age and sex (where appropriate). This model provides an overall pooled association between frailty and 4-year mortality. Adjustment for age and sex was needed, to make the distributions of the samples at each wave comparable over time, an important prerequisite for trend studies. In the second model time was added, to show trends in 4-year mortality across the period of observation. Finally, an interaction term between frailty and time was tested in Model 3. This interaction effect indicated whether there was a change over time in the frailty-mortality relationship. We did not consider other
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covariates, since the aim of our study was to show trends and not to explain them. We also tested a quadratic term for time, and an interaction effect between sex and frailty, but both were not statistically significant and not included in the final model. To get a better insight into the frailty-mortality relationship across the full period of observation, we also performed logistic regression analyses at each measurement wave, with frailty as independent variable and 4-year mortality as outcome, adjusted for age and sex.
We performed sensitivity analyses with a categorical time variable; additional adjustment for cohort and other covariates; prefrailty as separate category; and 3-year mortality as outcome (details in Web material). We also repeated the main analyses with a continuous frailty index score, using linear or logistic GEE analyses. The level of statistical significance was set at P <0.05 for main effects and P <0.10 for interaction effects. P values were 2-sided. All analyses were conducted using SPSS 26 (IBM corp, Armonk, NY, USA).
RESULTS
The characteristics of the study sample at each measurement wave are shown in Table 1. In the later measurement waves (i.e., the more recent samples of 64-84-year-olds) a higher frailty prevalence was observed (Figure 2). From these analyses, it was estimated that frailty (frailty index ≥0.25) increased from 21% in 1995-1996 to 28% in 2011-2012. Chronic conditions (including incontinence) and self-rated health are the frailty index domains that have increased the most over time (results not shown).
Between 1995 and 2016, the unadjusted 4-year mortality rate among older adults aged 64-84 years decreased from 15.4% to 7.9% (Figure 1). This was confirmed by the association between time and mortality in the adjusted GEE analyses (Table 2, Model 2). In the total sample (Odds Ratio (OR)= 0.97, 95% Confidence Interval (CI)= 0.95, 0.98) and in both men (OR= 0.96, 95% CI= 0.94, 0.97) and women (OR= 0.98, 95% CI= 0.96, 1.00), a statistically
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significant decrease in 4-year mortality rates was observed. The downward trend in 4-year mortality was observed in both non-frail and frail older adults. GEE analyses, adjusted for age and sex, showed that the estimated 4-year mortality reduced from 8.3% to 3.7% in non-frail older adults, and from 26.4% to 19.1% in frail older adults (Figure 3).
Table 2 and Figure 4 show the associations between frailty and 4-year mortality over time. In the GEE analysis, adjusted for age and sex (Table 2, Model 1), there is an statistically significant overall pooled association between frailty and 4-year mortality (OR= 2.79, 95% CI= 2.39, 3.26). This pooled association was present in both men (OR= 2.98, 95% CI= 2.42, 3.68) and women (OR= 2.53, 95% CI= 2.02, 3.18). The association remained after adding covariates in Model 2 and 3. Figure 4 illustrates how frailty is associated with mortality at each measurement wave. Across all measurement waves, frailty (in a model together with age
and sex) explained between 15% and 20% of the variance (Nagelkerke R2) in 4-year
mortality. There was no statistically significant interaction effect between frailty and time (OR= 1.02, 95% CI= 0.99, 1.05, P = 0.16). Although the interaction effect points into the direction of a slightly stronger frailty-mortality relationship in later measurement waves, it was small and not statistically significant, meaning that mortality risk in older adults aged 64-84 years with frailty compared to their non-frail counterparts remained the same over time.
We conducted several sensitivity analyses. First, using a categorical time variable in the GEE analyses instead of continuous time did not affect our main findings. The association between frailty and 4-year mortality became a bit stronger at later measurement waves, but this difference was not statistically significant (Web table 1). Second, additionally adjusting the analyses for cohort effects, educational level, partner status and smoking slightly changed the estimates, but the observed trends in frailty and the frailty-mortality relationship remained the same (Web tables 2 and 3). Third, adding prefrailty as a separate category in the analyses on 4-year mortality showed that there is a small association between prefrailty and 4-year
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mortality (OR= 1.34, 95% CI= 1.02, 1.76, P = 0.03) and that this association becomes slightly stronger over time (OR= 1.06, 95% CI= 1.00, 1.12, P= 0.05) (Web table 4). Fourth, the main results did not change when using 3-year mortality as outcome instead of 4-year mortality (Web table 5). Finally, sensitivity analyses (GEE, adjusted for age and sex) with a continuous frailty index score (not shown in table) confirmed our results. During the period 1995 to 2012, frailty scores increased among older adults aged 64-84 years (estimated mean 1995-96= 0.186, estimated mean 2011-12= 0.209, P time <0.001). There was also an overall pooled association between the continuous frailty index score and 4-year mortality (OR per 0.01 increase on the frailty index = 1.05, 95% CI= 1.04-1.06, P <0.001). The interaction term between frailty index score and time indicated that the frailty-mortality relationship became slightly stronger over time, but the OR was very small (OR= 1.00, 95% CI= 1.00-1.02, P = 0.03). Across measurement waves, the model with age, sex and a continuous frailty index
score explained between 17% and 21% of the variance (Nagelkerke R2) in 4-year mortality.
DISCUSSION
In this population-based study among older adults aged 64-84 years in the Netherlands, we investigated trends in frailty and its relationship with mortality across a period of 21 years (1995-2016). Three important conclusions can be drawn from our results: frailty prevalence rates have increased in more recent generations of older adults, 4-year mortality rates declined in both frail and non-frail older people, and there was a stable association between frailty and mortality.
Our results revealed that the proportion of frail older adults in the community is gradually increasing in more recent cohorts. This may be the result of increased life
expectancy, and the fact that people tend to live longer with chronic conditions than before (5). Our findings extend previous research from Hong Kong and the UK, which also found
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higher frailty levels in more recent generations of older adults (11-13). One study from Sweden observed stable frailty levels, but this work was based on a comparison of two cohorts of 70-year-olds from many years ago (measured in 1971 and 2000)(10).
Our study confirms the well-known association between frailty and mortality (4). Both men and women with frailty were at increased risk of 4-year mortality across the full study period. Although we observed slightly stronger frailty-mortality relationships in later measurement waves, this increase was not statistically significant. This means that, while in recent decades favorable trends have been observed in the excess mortality of various chronic conditions, such as heart disease (6), the excess mortality rates of frailty have remained the same in different historical periods. A previous study in the UK found the same when comparing two cohorts of older adults aged 65 years and over in 1991 and 2011. On the contrary, work from Sweden showed that the frailty-mortality association became weaker over time (10). However, this study compared two cohorts 30 years apart, in a very specific age-group (70-year-olds) and – as mentioned before – in a different historical period.
The results of our study have implications for clinical practice and public health. We observed an increase in the prevalence of frailty, which in turn may have impact on healthcare use. Combined with the aging of the population, this impact could be even stronger, as the number of older adults in society is growing (5), of which a larger proportion will be frail. Therefore, it is likely that the frailty-related burden for the healthcare system will increase. Despite many interventional programs focused on reducing frailty and its adverse outcomes (27-29), these initiatives do not seem to have resulted in decreased excess mortality rates of frailty in the past two decades. Therefore, much more research is needed to identify
interventions that can effectively prevent frailty progression and improve health outcomes in frail older adults. At the same time, it remains to be seen whether excess mortality rates of frailty can be reduced in the same way as we have seen for various chronic conditions in the
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past few decades. Perhaps, it is much more difficult to reverse frailty – an indicator of biological aging - than the impact of a single disease (27).
Our study has several strengths. We used nationally representative data from a large study among older adults in the Netherlands. The cohort-sequential design, with exactly the same measurement instruments at each measurement wave, allowed for identification of secular trends in frailty. Moreover, this was the first study to investigate trends in frailty and its association with mortality over an extended time period that made use of data from
multiple time-points only 3 years apart. Therefore, our study is better able to capture trends in specific periods, compared to previous studies that made use of only two time-points 20 to 30 years apart (10, 11) or data from age-cohorts that were all measured at the same moment (12).
Nevertheless, this study also has some limitations. First, the current study was only descriptive. Explaining trends is an important next step to understand secular trends in frailty and its relationship with mortality. However, it is also rather complex to explain three
different trends (in predictor, outcome, and association) within the same study. We therefore considered this as beyond the scope of the current study. Second, our analyses were done on samples that were partly overlapping. A design with multiple independent samples at various time points, with longitudinal data on mortality, would be more ideal. However, such data on frailty is not available. LASA is one of the few studies worldwide that allows for examination of trends in frailty over a period of more than 20 years, with a cohort-sequential design with partly independent samples (17). Third, we interpret our findings as trends, but they may also partially come from changes in reporting behavior in more recent cohorts. It is well-known that higher expectations of healthcare and lower tolerance of health problems may lead to changes in self-report in more recent generations of older adults (30, 31). At the same time, this does not apply to all items of the frailty index, as many of them are based on standardized instruments or performance measures. Fourth, we have not examined the population
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attributable fraction of mortality explained by frailty. Even though the association between frailty and 4-year mortality did not change over time, the rise in the prevalence of frailty combined with decreased overall mortality may have led to changes in the population attributable fraction of frailty for mortality. This should be addressed in future research. Finally, in our study we used only one out of many available frailty instruments (32, 33). Although the frailty index is one of the most commonly used and widely validated frailty instruments, it would be interesting to see whether the results would be the same with other important frailty measures, such as the physical frailty phenotype (34).
To conclude, this trend study among older adults aged 64-84 years in the Netherlands indicated that higher frailty prevalence rates were observed in more recent generations of older adults, together with a stable trend in the frailty-mortality relationship. This means that the proportion of frail older adults in the community is increasing, while the excess mortality rates of frailty remained the same. This is important information for health policy makers and clinical practice, as it shows that continued efforts are needed to reduce frailty and its negative health consequences.
ACKNOWLEDGMENTS
Author affiliations: Department of Epidemiology and Data Science, Amsterdam Public Health research institute, Amsterdam UMC - location VU University Medical Center, Amsterdam, the Netherlands (Emiel O. Hoogendijk, Dorly D.J.H. Deeg, Martijn Huisman); Institute of Social Medicine and Epidemiology, Medical University of Graz, Graz, Austria (Erwin Stolz); Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands (Richard C. Oude Voshaar, Hans W. Jeuring); Department of Sociology, Faculty of Social Sciences, Vrije Universiteit Amsterdam, the Netherlands (Martijn Huisman).
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Funding: Emiel O. Hoogendijk was supported by an NWO/ZonMw Veni fellowship, grant number 91618067. The Longitudinal Aging Study Amsterdam (LASA) is largely supported by a grant from the Netherlands Ministry of Health, Welfare and Sports, Directorate of Long-Term Care.
Data availability: The datasets generated and/or analyzed during the current study are not publicly available due to confidentiality, but the data underlying the results presented in this study are available from the Longitudinal Aging Study Amsterdam (LASA). More
information on data requests can be found on the LASA website: www.lasa-vu.nl.
Conflicts of interest: None declared.
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18 Table 1. Characteristics of the Study Sample at Each Measurement Wave for Participants Aged 64-84 Years in the Longitudinal Aging Study Amsterdam, the Netherlands, 1995-2012
Abbrevations: SD, standard deviation; CI, confidence interval a
Prevalence derived from Generalized Estimating Equation analyses, adjusted for age and sex b
Prefrailty: frailty index 0.08-0.24 c
Frailty: frailty index ≥0.25
Wave No. Age In years Sex (Female) Prefrailtya,b Frailtya,c
Mean (SD) % % 95% CI % 95% CI T1: 1995-1996 1,549 74.0 (6.1) 52.8 60.2 57.7, 62.6 20.9 19.1, 23.0 T2: 1998-1999 1,418 73.6 (6.0) 55.2 60.0 57.5, 62.5 24.0 22.0, 26.2 T3: 2001-2002 1,225 73.4 (5.7) 55.3 60.4 57.7, 63.0 25.4 23.2, 27.8 T4: 2005-2006 1,221 72.8 (5.8) 54.9 59.0 56.3, 61.1 26.8 24.4, 29.3 T5: 2008-2009 1,188 73.2 (6.0) 54.8 58.6 55.8, 61.3 25.7 23.4, 28.3 T6: 2011-2012 1,141 73.0 (5.8) 54.6 57.2 54.3, 60.0 27.9 25.3, 30.6
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19 Table 2. Generalized Estimating Equation Analyses: Associations Between Frailty and 4-Year Mortality Over Time for Participants Aged 64-84 Years in the Longitudinal Aging Study Amsterdam, the Netherlands, 1995-2016a,b,c
Variables Total Men Women
OR 95% CI P OR 95% CI P OR 95% CI P Model 1 Frailty 2.79 2.39, 3.26 <0.001 2.98 2.42, 3.68 <0.001 2.53 2.02, 3.18 <0.001 Model 2 Frailty 2.88 2.47, 3.37 <0.001 3.13 2.54, 3.87 <0.001 2.57 2.05, 3.24 <0.001 Time (years) 0.97 0.95, 0.98 <0.001 0.96 0.94, 0.97 <0.001 0.98 0.96, 1.00 <0.05 Model 3 Frailty 2.56 2.02, 3.23 <0.001 2.88 2.08, 4.01 <0.001 2.34 1.64, 3.33 <0.001 Time (years) 0.96 0.94, 0.97 <0.001 0.95 0.93, 0.97 <0.001 0.97 0.94, 1.00 <0.05 Frailty × Time 1.02 0.99, 1.05 0.16 1.01 0.98, 1.05 0.51 1.01 0.98, 1.05 0.47
Abbreviations: OR, odds ratio; CI, confidence interval a
The analyses include 7,742 observations of 2,874 respondents b
All models were adjusted for age and sex; sex adjustment only in the analysis of the total population c
Frailty: frailty index ≥0.25
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20 Figure 1. Design of the trend study and 4-year mortality rates among older adults aged 64-84 years participating in the Longitudinal Aging Study Amsterdam (LASA), 1995-2016.
Figure 2. Trends in frailty by sex among older adults aged 64-84 years participating in the Longitudinal Aging Study Amsterdam, the Netherlands, 1995-2012. Estimated frailty prevalence (frailty index ≥0.25) derived from Generalized Estimating Equation analyses, adjusted for age and sex. Sex adjustment only in the analysis of the total population.
Figure 3. Trends in 4-year mortality by frailty (frailty index ≥0.25) and sex among older adults aged 64-84 years participating in the Longitudinal Aging Study Amsterdam, 1995-2016. Solid lines represent people with frailty (● men, ■ total group, ▲women), dashed lines represent people without frailty (● men, ■ total group, ▲women). Estimated proportion 4-year mortality derived from
Generalized Estimating Equation analyses, adjusted for age and sex. Sex adjustment only in the analysis of the total population.
Figure 4. Association between frailty (frailty index ≥0.25) and 4-year mortality across waves and in a pooled analysis, for older adults aged 64-84 years participating in the Longitudinal Aging Study Amsterdam, 1995-2016. Generalized Estimating Equation analysis was used for the pooled analysis, and logistic regression for the wave-specific analysis, all adjusted for age and sex. Abbreviations: OR, odds ratio.
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